Embedded Vision: Mastering Advanced Techniques for Real-Time Image Processing and Analysis

Learn the fundamentals and applications of embedded vision systems, covering various industries and practical examples. Key Features • Comprehensive guide to embedded vision systems • Detailed coverage of design and implementation across industries • Practical real-time examples for hands-on learning Book DescriptionEmbedded vision integrates computer vision into machines using algorithms to interpret images or videos. This book serves as an introductory guide for designing vision-enabled embedded products, with applications in AI, machine learning, industrial, medical, automotive, and more. It covers hardware architecture, software algorithms, applications, and advancements in cameras, processors, and sensors.

The course begins with an overview of embedded vision, followed by industrial and medical vision applications. It then delves into video analytics, digital image processing, and camera-image sensors. Subsequent chapters cover embedded vision processors, computer vision, and AI integration. The final chapter presents real-time vision-based examples.

Understanding these concepts is vital for developing advanced vision-enabled machines. This book takes readers from the basics to advanced topics, blending theoretical knowledge with practical applications. It is an essential resource for mastering embedded vision technology across various industries.What you will learn • Understand the basics of embedded vision systems • Design and implement vision processors • Apply digital image processing techniques • Utilize AI in vision systems • Develop real-time vision applications • Integrate vision sensors and cameras effectively Who this book is for The ideal audience for this book includes engineers, developers, and researchers working in the field of embedded vision systems. A basic understanding of computer vision and digital image processing is recommended.

© 2024 Packt Publishing (Ebook): 9781836645009

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Ebook: 2 augustus 2024